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      name:  <unnamed>
       log:  /Users/seungbinpark/Dropbox/R - TPP/ISQ/ISQ -final version/replication files/Bearce & Park ISQ - 
> replication log.log
  log type:  text
 opened on:  14 Jun 2024, 13:28:45

. do "/var/folders/j_/rkkb6jh133xbd3x2t91ql1p80000gn/T//SD00697.000000"

. 
. * Replication Do File for David Bearce & Seungbin Park "Mass Attitudes about International Trade Agreements:
>  Positive Messages and the Trans-Pacific Partnership" ISQ.
. 
. * Analysis runs using Stata 16.
. 
. * Data source: Original survey experiment data collected in 2023.
. 
. * Tables referred to in the main text are sequentially numbered. Tables located in the appendix have titles 
> prefixed by "A" (e.g., Table A1).
. 
. 
. 
. 
. *******************************************************************************
. *Table A1: Descriptive Statistics/Balance Table.
.         **Sample: Full
. sum FavorTPP RejoinTPP RatifyTPP WroteStatement FavorNAFTA Education Income Age Female White Black Asian His
> panic Democrat Independent Republican 

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    FavorTPP |      2,404     .609401    1.084301         -2          2
   RejoinTPP |      2,404    .6114809    1.198751         -2          2
   RatifyTPP |      2,404    .6073211    1.151328         -2          2
WroteState~t |      2,404     .186772    .3898095          0          1
  FavorNAFTA |      2,404    .7445923    1.174564         -2          2
-------------+---------------------------------------------------------
   Education |      2,397    2.376721    1.107499          0          4
      Income |      2,334    9.078835    6.782693          1         24
         Age |      2,404    44.57737    16.53313         18         94
      Female |      2,404    .5108153     .499987          0          1
       White |      2,404    .7412646    .4380311          0          1
-------------+---------------------------------------------------------
       Black |      2,404    .1152246    .3193593          0          1
       Asian |      2,404    .0507488    .2195299          0          1
    Hispanic |      2,404    .1148087    .3188572          0          1
    Democrat |      2,404    .4434276    .4968926          0          1
 Independent |      2,404    .2233777    .4165961          0          1
-------------+---------------------------------------------------------
  Republican |      2,404    .3311148    .4707122          0          1

. 
.         **Sample: Control, InfoOnly, LowerPrices, & CounterChina  
. sum FavorTPP RejoinTPP RatifyTPP WroteStatement FavorNAFTA Education Income Age Female White Black Asian His
> panic Democrat Independent Republican if control==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    FavorTPP |        600    .4416667    1.021438         -2          2
   RejoinTPP |        600    .4116667     1.14501         -2          2
   RatifyTPP |        600    .4716667    1.091413         -2          2
WroteState~t |        600    .1533333    .3606091          0          1
  FavorNAFTA |        600    .7516667    1.148417         -2          2
-------------+---------------------------------------------------------
   Education |        600        2.35    1.113358          0          4
      Income |        579    8.759931    6.644407          1         24
         Age |        600    44.73167    16.48948         18         92
      Female |        600          .5    .5004172          0          1
       White |        600        .745    .4362249          0          1
-------------+---------------------------------------------------------
       Black |        600    .1083333    .3110603          0          1
       Asian |        600    .0516667    .2215379          0          1
    Hispanic |        600          .1    .3002503          0          1
    Democrat |        600    .4466667    .4975622          0          1
 Independent |        600         .22    .4145919          0          1
-------------+---------------------------------------------------------
  Republican |        600    .3316667    .4712048          0          1

. 
. sum FavorTPP RejoinTPP RatifyTPP WroteStatement FavorNAFTA Education Income Age Female White Black Asian His
> panic Democrat Independent Republican if InfoOnly ==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    FavorTPP |        601    .5449251    1.126785         -2          2
   RejoinTPP |        601    .5856905     1.23142         -2          2
   RatifyTPP |        601    .5041597    1.197386         -2          2
WroteState~t |        601    .1830283    .3870118          0          1
  FavorNAFTA |        601    .8153078    1.212779         -2          2
-------------+---------------------------------------------------------
   Education |        598    2.381271    1.124622          0          4
      Income |        587    8.875639    6.875281          1         24
         Age |        601    44.97171     16.6206         18         84
      Female |        601    .5108153    .5002994          0          1
       White |        601    .7237937    .4474926          0          1
-------------+---------------------------------------------------------
       Black |        601     .124792    .3307582          0          1
       Asian |        601    .0449251    .2073123          0          1
    Hispanic |        601    .1114809    .3149888          0          1
    Democrat |        601     .452579    .4981608          0          1
 Independent |        601      .21797    .4132108          0          1
-------------+---------------------------------------------------------
  Republican |        601     .327787    .4697978          0          1

. 
. sum FavorTPP RejoinTPP RatifyTPP WroteStatement FavorNAFTA Education Income Age Female White Black Asian His
> panic Democrat Independent Republican if LowerPrices ==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    FavorTPP |        601    .6921797    1.144227         -2          2
   RejoinTPP |        601    .7004992    1.232944         -2          2
   RatifyTPP |        601    .6838602     1.19578         -2          2
WroteState~t |        601    .2312812     .422003          0          1
  FavorNAFTA |        601    .6655574    1.173156         -2          2
-------------+---------------------------------------------------------
   Education |        601    2.452579    1.059008          0          4
      Income |        587    9.667802    6.811231          1         24
         Age |        601    44.29118    16.67203         18         94
      Female |        601    .5241265    .4998336          0          1
       White |        601    .7537438    .4311884          0          1
-------------+---------------------------------------------------------
       Black |        601    .1131448    .3170335          0          1
       Asian |        601    .0565724    .2312161          0          1
    Hispanic |        601    .1164725    .3210579          0          1
    Democrat |        601    .4259567    .4948991          0          1
 Independent |        601    .2296173    .4209371          0          1
-------------+---------------------------------------------------------
  Republican |        601    .3394343    .4739117          0          1

. 
. sum FavorTPP RejoinTPP RatifyTPP WroteStatement FavorNAFTA Education Income Age Female White Black Asian His
> panic Democrat Independent Republican if CounterChina ==1

    Variable |        Obs        Mean    Std. Dev.       Min        Max
-------------+---------------------------------------------------------
    FavorTPP |        602    .7583056    1.012471         -2          2
   RejoinTPP |        602    .7475083    1.157786         -2          2
   RatifyTPP |        602     .769103    1.092179         -2          2
WroteState~t |        602     .179402    .3840076          0          1
  FavorNAFTA |        602    .7458472     1.16101         -2          2
-------------+---------------------------------------------------------
   Education |        598    2.322742    1.130297          0          4
      Income |        581    9.006885    6.776732          1         24
         Age |        602    44.31561    16.38036         18         85
      Female |        602    .5083056    .5003468          0          1
       White |        602    .7425249    .4376069          0          1
-------------+---------------------------------------------------------
       Black |        602    .1146179    .3188252          0          1
       Asian |        602    .0498339    .2177826          0          1
    Hispanic |        602    .1312292    .3379317          0          1
    Democrat |        602     .448505    .4977548          0          1
 Independent |        602    .2259136    .4185303          0          1
-------------+---------------------------------------------------------
  Republican |        602    .3255814    .4689813          0          1

. 
. 
. 
. *******************************************************************************
. *Table A2: Models of FavorTPP.
. 
.         **Model 1: ITT Sample with Covariates
. reg FavorTPP InfoOnly LowerPrices CounterChina FavorNAFTA Education Income Age Female White Black Asian Hisp
> anic Democrat Republican i.state, cluster (state)

Linear regression                               Number of obs     =      2,327
                                                F(13, 51)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.3166
                                                Root MSE          =     .91202

                                             (Std. Err. adjusted for 52 clusters in state)
------------------------------------------------------------------------------------------
                         |               Robust
                FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                InfoOnly |   .0674269   .0563081     1.20   0.237    -.0456162    .1804701
             LowerPrices |   .2832398   .0630946     4.49   0.000     .1565721    .4099074
            CounterChina |   .3010389   .0480963     6.26   0.000     .2044817    .3975962
              FavorNAFTA |   .4002341   .0197175    20.30   0.000     .3606495    .4398187
               Education |   .0080999   .0227411     0.36   0.723    -.0375548    .0537546
                  Income |   .0054778   .0025276     2.17   0.035     .0004033    .0105522
                     Age |   -.004778   .0016713    -2.86   0.006    -.0081332   -.0014228
                  Female |  -.0297907   .0347555    -0.86   0.395    -.0995653     .039984
                   White |   .0865097   .0644702     1.34   0.186    -.0429196     .215939
                   Black |   .0592706   .0869234     0.68   0.498    -.1152354    .2337766
                   Asian |   .1027453   .1081275     0.95   0.346    -.1143297    .3198203
                Hispanic |   .0364841   .0595889     0.61   0.543    -.0831457    .1561139
                Democrat |   .2441862   .0533849     4.57   0.000     .1370116    .3513608
              Republican |  -.3243001   .0629999    -5.15   0.000    -.4507776   -.1978226
                         |
                   state |
              Alaska AK  |  -.4032547   .0279979   -14.40   0.000    -.4594629   -.3470466
             Arizona AZ  |   .0936422   .0272053     3.44   0.001     .0390253    .1482591
            Arkansas AR  |   .1031866   .0181608     5.68   0.000     .0667272    .1396459
          California CA  |  -.0005035   .0310826    -0.02   0.987    -.0629045    .0618975
            Colorado CO  |  -.2294686   .0213923   -10.73   0.000    -.2724155   -.1865217
         Connecticut CT  |   .1120228   .0321846     3.48   0.001     .0474094    .1766362
            Delaware DE  |   .2057371   .0253932     8.10   0.000     .1547581    .2567161
District of Columbia DC  |  -.4570361   .0494205    -9.25   0.000    -.5562518   -.3578204
             Florida FL  |   .0400499   .0211597     1.89   0.064    -.0024299    .0825298
             Georgia GA  |  -.0211956   .0197889    -1.07   0.289    -.0609235    .0185323
              Hawaii HI  |  -.3798509   .0474066    -8.01   0.000    -.4750236   -.2846783
               Idaho ID  |   .2827033   .0182507    15.49   0.000     .2460635    .3193432
            Illinois IL  |  -.0243624   .0185732    -1.31   0.195    -.0616497    .0129249
             Indiana IN  |   .0784445   .0211766     3.70   0.001     .0359306    .1209583
                Iowa IA  |   .0775553   .0300048     2.58   0.013     .0173182    .1377924
              Kansas KS  |  -.0554712   .0239143    -2.32   0.024    -.1034812   -.0074612
            Kentucky KY  |   .1651908   .0181465     9.10   0.000     .1287601    .2016214
           Louisiana LA  |   .0133011   .0151223     0.88   0.383    -.0170581    .0436603
               Maine ME  |  -.8526824   .0294012   -29.00   0.000    -.9117077   -.7936571
            Maryland MD  |  -.0323922    .024485    -1.32   0.192    -.0815479    .0167634
       Massachusetts MA  |  -.2194869   .0223316    -9.83   0.000    -.2643195   -.1746544
            Michigan MI  |  -.0614562   .0189584    -3.24   0.002    -.0995167   -.0233956
           Minnesota MN  |   .0980447   .0147434     6.65   0.000     .0684461    .1276433
         Mississippi MS  |   .4748425   .0236026    20.12   0.000     .4274582    .5222268
            Missouri MO  |   .1093162   .0112451     9.72   0.000     .0867408    .1318916
             Montana MT  |  -.7072671   .0349183   -20.25   0.000    -.7773686   -.6371656
            Nebraska NE  |   .2429327   .0308708     7.87   0.000      .180957    .3049085
              Nevada NV  |   .0724367   .0241965     2.99   0.004     .0238601    .1210132
       New Hampshire NH  |   .0904074   .0324148     2.79   0.007     .0253321    .1554828
          New Jersey NJ  |  -.1170767   .0230214    -5.09   0.000     -.163294   -.0708594
          New Mexico NM  |  -.3812791    .033496   -11.38   0.000     -.448525   -.3140331
            New York NY  |   .0724525   .0247083     2.93   0.005     .0228485    .1220565
      North Carolina NC  |  -.0422413   .0109534    -3.86   0.000    -.0642311   -.0202514
       North Dakota  ND  |  -.0256514   .0299937    -0.86   0.396    -.0858663    .0345634
                Ohio OH  |   .0941002   .0153562     6.13   0.000     .0632713    .1249292
            Oklahoma OK  |  -.1357786   .0181531    -7.48   0.000    -.1722225   -.0993347
              Oregon OR  |   .2548715   .0227497    11.20   0.000     .2091995    .3005435
        Pennsylvania PA  |  -.1343539   .0176506    -7.61   0.000     -.169789   -.0989187
         Puerto Rico PR  |  -.2873747   .0864848    -3.32   0.002    -.4610001   -.1137493
        Rhode Island RI  |   .1636205   .0315719     5.18   0.000     .1002373    .2270037
      South Carolina SC  |  -.0096385   .0254151    -0.38   0.706    -.0606616    .0413845
        South Dakota SD  |   .1235778   .0230126     5.37   0.000      .077378    .1697776
           Tennessee TN  |    .092334   .0114164     8.09   0.000     .0694145    .1152535
               Texas TX  |   .0589974   .0223763     2.64   0.011      .014075    .1039197
                Utah UT  |   .1565763   .0194707     8.04   0.000     .1174873    .1956654
             Vermont VT  |   .1228587   .0369591     3.32   0.002     .0486602    .1970571
            Virginia VA  |  -.0123991   .0237852    -0.52   0.604    -.0601498    .0353517
          Washington WA  |   .1263715   .0202555     6.24   0.000     .0857069    .1670362
       West Virginia WV  |   .1421725   .0199133     7.14   0.000     .1021949    .1821501
           Wisconsin WI  |   .0544392   .0274745     1.98   0.053    -.0007182    .1095966
             Wyoming WY  |  -.3214133    .044786    -7.18   0.000    -.4113249   -.2315017
                         |
                   _cons |   .2079114    .091419     2.27   0.027     .0243801    .3914427
------------------------------------------------------------------------------------------

. estimates store M1 

. 
.         **Model 2: ITT Sample without Covariates
. reg FavorTPP InfoOnly LowerPrices CounterChina, cluster (state)

Linear regression                               Number of obs     =      2,404
                                                F(3, 51)          =      10.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0130
                                                Root MSE          =     1.0779

                                 (Std. Err. adjusted for 52 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .1032585   .0776287     1.33   0.189    -.0525876    .2591045
 LowerPrices |    .250513   .0802446     3.12   0.003     .0894153    .4116108
CounterChina |    .316639   .0575966     5.50   0.000     .2010089    .4322691
       _cons |   .4416667   .0459159     9.62   0.000     .3494867    .5338466
------------------------------------------------------------------------------

. estimates store M2

. 
.         **Model 3: ATT Sample
. reg FavorTPP InfoOnly LowerPrices CounterChina if ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =      1,563
                                                F(3, 50)          =      15.73
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0295
                                                Root MSE          =     1.0663

                                 (Std. Err. adjusted for 51 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |    .143239   .0875616     1.64   0.108    -.0326337    .3191117
 LowerPrices |   .4378713     .08681     5.04   0.000     .2635083    .6122343
CounterChina |   .4008939    .066444     6.03   0.000     .2674373    .5343505
       _cons |   .4416667   .0459402     9.61   0.000     .3493931    .5339402
------------------------------------------------------------------------------

. estimates store M4

. 
. 
. 
. *******************************************************************************
. *Figure 1: Treatment Effects with 95% Confidence Intervals on FavorTPP from Table A2.
. 
. coefplot (M1, label(ITT sample with covariates) pstyle(p1) msymbol(O)) (M2, label(ITT sample without covaria
> tes) pstyle(p1) msymbol(S)) (M4, label(ATT sample) pstyle(p1) msymbol(D)), keep (InfoOnly LowerPrices Counte
> rChina) xline(0) byopts(cols(1)) subtitle(, justification(left)) graphregion(fcolor(white)) xscale(range(-0.
> 1 0.7)) xlabel(-0.1(0.1)0.7)

. 
. 
. 
. *******************************************************************************
. *Table A3: Disaggregating FavorTPP Using ITT Sample.
. 
.         **Model 1: Model of RejoinTPP
. reg RejoinTPP InfoOnly LowerPrices CounterChina, cluster (state)

Linear regression                               Number of obs     =      2,404
                                                F(3, 51)          =       6.96
                                                Prob > F          =     0.0005
                                                R-squared         =     0.0117
                                                Root MSE          =     1.1925

                                 (Std. Err. adjusted for 52 clusters in state)
------------------------------------------------------------------------------
             |               Robust
   RejoinTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .1740238   .0912311     1.91   0.062    -.0091303     .357178
 LowerPrices |   .2888325   .0882755     3.27   0.002     .1116121    .4660529
CounterChina |   .3358416   .0766697     4.38   0.000     .1819208    .4897624
       _cons |   .4116667   .0566423     7.27   0.000     .2979525    .5253808
------------------------------------------------------------------------------

. 
.         **Model 2: Model of RatifyTPP
. reg RatifyTPP InfoOnly LowerPrices CounterChina, cluster (state)

Linear regression                               Number of obs     =      2,404
                                                F(3, 51)          =       9.06
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0115
                                                Root MSE          =     1.1454

                                 (Std. Err. adjusted for 52 clusters in state)
------------------------------------------------------------------------------
             |               Robust
   RatifyTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .0324931   .0713063     0.46   0.651    -.1106602    .1756464
 LowerPrices |   .2121936   .0811584     2.61   0.012     .0492613    .3751258
CounterChina |   .2974363   .0589963     5.04   0.000     .1789963    .4158764
       _cons |   .4716667   .0442973    10.65   0.000     .3827361    .5605972
------------------------------------------------------------------------------

. 
. 
. 
. 
. *******************************************************************************
. *Table 1: Percentage in Support of Rejoining and Ratifying the Trans-Pacific Partnership from Table A3.
.         **RejoinTPP Column
. tab RejoinTPP if Control_InfoOnly == 1

      Favor |
  rejoining |
       TPP: |
 2=strongly |
  favor ... |
-2=strongly |
     oppose |      Freq.     Percent        Cum.
------------+-----------------------------------
         -2 |        103        8.58        8.58
         -1 |         81        6.74       15.32
          0 |        443       36.89       52.21
          1 |        262       21.82       74.02
          2 |        312       25.98      100.00
------------+-----------------------------------
      Total |      1,201      100.00

. di (262+312)/1201*100
47.793505

. 
. tab RejoinTPP if LowerPrices_CounterChina == 1

      Favor |
  rejoining |
       TPP: |
 2=strongly |
  favor ... |
-2=strongly |
     oppose |      Freq.     Percent        Cum.
------------+-----------------------------------
         -2 |         87        7.23        7.23
         -1 |         73        6.07       13.30
          0 |        330       27.43       40.73
          1 |        308       25.60       66.33
          2 |        405       33.67      100.00
------------+-----------------------------------
      Total |      1,203      100.00

. di (308+405)/1203*100
59.268495

. 
. tab RejoinTPP if control==1  

      Favor |
  rejoining |
       TPP: |
 2=strongly |
  favor ... |
-2=strongly |
     oppose |      Freq.     Percent        Cum.
------------+-----------------------------------
         -2 |         50        8.33        8.33
         -1 |         40        6.67       15.00
          0 |        253       42.17       57.17
          1 |        127       21.17       78.33
          2 |        130       21.67      100.00
------------+-----------------------------------
      Total |        600      100.00

. di (127+130)/600*100
42.833333

. 
. 
.         *RatifyTPP Column
. tab RatifyTPP if Control_InfoOnly == 1 

   Congress |
     should |
ratify TPP: |
    2=Agree |
   strongly |
        ... |
-2=Disagree |
   strongly |      Freq.     Percent        Cum.
------------+-----------------------------------
         -2 |         89        7.41        7.41
         -1 |         95        7.91       15.32
          0 |        433       36.05       51.37
          1 |        309       25.73       77.10
          2 |        275       22.90      100.00
------------+-----------------------------------
      Total |      1,201      100.00

. di (309+275)/1201*100
48.626145

. 
. tab RatifyTPP if LowerPrices_CounterChina == 1

   Congress |
     should |
ratify TPP: |
    2=Agree |
   strongly |
        ... |
-2=Disagree |
   strongly |      Freq.     Percent        Cum.
------------+-----------------------------------
         -2 |         74        6.15        6.15
         -1 |         70        5.82       11.97
          0 |        344       28.60       40.57
          1 |        338       28.10       68.66
          2 |        377       31.34      100.00
------------+-----------------------------------
      Total |      1,203      100.00

. di (338+377)/1203*100
59.434746

. 
. tab RatifyTPP if control==1 

   Congress |
     should |
ratify TPP: |
    2=Agree |
   strongly |
        ... |
-2=Disagree |
   strongly |      Freq.     Percent        Cum.
------------+-----------------------------------
         -2 |         34        5.67        5.67
         -1 |         52        8.67       14.33
          0 |        239       39.83       54.17
          1 |        147       24.50       78.67
          2 |        128       21.33      100.00
------------+-----------------------------------
      Total |        600      100.00

. di (147+128)/600*100
45.833333

. 
. 
. 
. *******************************************************************************
. *Table A4: Split ITT Sample Models of FavorTPP.
. 
.         **Model 1: Sample of Less Education
. reg FavorTPP InfoOnly LowerPrices CounterChina if Bachelors==0, cluster (state) 

Linear regression                               Number of obs     =      1,318
                                                F(3, 51)          =       4.27
                                                Prob > F          =     0.0092
                                                R-squared         =     0.0101
                                                Root MSE          =     1.0332

                                 (Std. Err. adjusted for 52 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |    .070221   .0829516     0.85   0.401    -.0963113    .2367533
 LowerPrices |    .219651   .0778925     2.82   0.007     .0632753    .3760266
CounterChina |   .2515624   .0759346     3.31   0.002     .0991174    .4040075
       _cons |    .441791   .0536578     8.23   0.000     .3340685    .5495136
------------------------------------------------------------------------------

. estimates store M4b

. 
.         **Model 2: Sample of More Education  
. reg FavorTPP InfoOnly LowerPrices CounterChina if Bachelors==1, cluster (state)

Linear regression                               Number of obs     =      1,079
                                                F(3, 49)          =      12.61
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0164
                                                Root MSE          =     1.1321

                                 (Std. Err. adjusted for 50 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .1433962   .1112667     1.29   0.204    -.0802026    .3669951
 LowerPrices |   .2854409   .1096353     2.60   0.012     .0651205    .5057613
CounterChina |   .3880786   .0682707     5.68   0.000     .2508835    .5252737
       _cons |   .4415094    .064741     6.82   0.000     .3114075    .5716113
------------------------------------------------------------------------------

. estimates store M4a

. 
.         **Model 3: Sample of Republican
. reg FavorTPP InfoOnly LowerPrices CounterChina if Republican==1, cluster (state)

Linear regression                               Number of obs     =        796
                                                F(3, 49)          =       2.25
                                                Prob > F          =     0.0941
                                                R-squared         =     0.0090
                                                Root MSE          =     1.1709

                                 (Std. Err. adjusted for 50 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |  -.0241563   .1331767    -0.18   0.857    -.2917849    .2434723
 LowerPrices |   .1073382   .1336889     0.80   0.426    -.1613198    .3759961
CounterChina |    .259499   .1143992     2.27   0.028     .0296052    .4893929
       _cons |    .120603   .0913081     1.32   0.193    -.0628876    .3040936
------------------------------------------------------------------------------

. estimates store M4d

. 
.         **Model 4: Sample of Not Republican
. reg FavorTPP InfoOnly LowerPrices CounterChina if Republican==0, cluster (state)

Linear regression                               Number of obs     =      1,608
                                                F(3, 51)          =      10.00
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0203
                                                Root MSE          =     .96857

                                 (Std. Err. adjusted for 52 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .1626164   .0830067     1.96   0.056    -.0040265    .3292592
 LowerPrices |    .329733      .0875     3.77   0.000     .1540694    .5053966
CounterChina |   .3398892   .0642871     5.29   0.000     .2108274     .468951
       _cons |   .6009975   .0533553    11.26   0.000     .4938823    .7081127
------------------------------------------------------------------------------

. estimates store M4c

. 
. 
. 
. *******************************************************************************
. *Table A5: FavorTPP Interaction Models.
. 
.         **Model 1: ITT Sample and Interaction with Bachelors
. reg FavorTPP InfoOnly LowerPrices CounterChina Bachelors InfoOnlyXBachelors LowerPricesXBachelors CounterChi
> naXBachelors, cluster (state)

Linear regression                               Number of obs     =      2,397
                                                F(7, 51)          =       8.66
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0143
                                                Root MSE          =     1.0788

                                           (Std. Err. adjusted for 52 clusters in state)
----------------------------------------------------------------------------------------
                       |               Robust
              FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
              InfoOnly |    .070221   .0829784     0.85   0.401    -.0963651     .236807
           LowerPrices |    .219651   .0779176     2.82   0.007     .0632248    .3760771
          CounterChina |   .2515624   .0759591     3.31   0.002     .0990682    .4040567
             Bachelors |  -.0002816   .0738963    -0.00   0.997    -.1486346    .1480714
    InfoOnlyXBachelors |   .0731753    .113958     0.64   0.524     -.155605    .3019555
 LowerPricesXBachelors |     .06579   .0956253     0.69   0.495    -.1261858    .2577657
CounterChinaXBachelors |   .1365162   .0887396     1.54   0.130     -.041636    .3146683
                 _cons |    .441791   .0536751     8.23   0.000     .3340337    .5495484
----------------------------------------------------------------------------------------

. 
.         **Model 2: ITT Sample and Interaction with Republican
. reg FavorTPP InfoOnly LowerPrices CounterChina Republican InfoOnlyXRepublican  LowerPricesXRepublican Counte
> rChinaXRepublican, cluster (state)

Linear regression                               Number of obs     =      2,404
                                                F(7, 51)          =      37.70
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0830
                                                Root MSE          =     1.0398

                                            (Std. Err. adjusted for 52 clusters in state)
-----------------------------------------------------------------------------------------
                        |               Robust
               FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
               InfoOnly |   .1626164   .0830502     1.96   0.056    -.0041139    .3293466
            LowerPrices |    .329733   .0875459     3.77   0.000     .1539772    .5054887
           CounterChina |   .3398892   .0643208     5.28   0.000     .2107597    .4690187
             Republican |  -.4803945   .1135269    -4.23   0.000    -.7083092   -.2524798
    InfoOnlyXRepublican |  -.1867727   .1513524    -1.23   0.223    -.4906253      .11708
 LowerPricesXRepublican |  -.2223948   .1540069    -1.44   0.155    -.5315766     .086787
CounterChinaXRepublican |  -.0803902   .1314942    -0.61   0.544    -.3443759    .1835956
                  _cons |   .6009975   .0533833    11.26   0.000     .4938261    .7081689
-----------------------------------------------------------------------------------------

. 
.         **Model 3: ATT Sample and Interaction with Republican
. reg FavorTPP InfoOnly LowerPrices CounterChina Republican InfoOnlyXRepublican  LowerPricesXRepublican Counte
> rChinaXRepublican if ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =      1,563
                                                F(7, 50)          =      39.54
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1038
                                                Root MSE          =      1.026

                                            (Std. Err. adjusted for 51 clusters in state)
-----------------------------------------------------------------------------------------
                        |               Robust
               FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
               InfoOnly |   .2475502   .0870085     2.85   0.006     .0727885    .4223119
            LowerPrices |   .5103886   .1111676     4.59   0.000     .2871019    .7336754
           CounterChina |   .4394065   .0785656     5.59   0.000     .2816029    .5972101
             Republican |  -.4803945   .1136381    -4.23   0.000    -.7086433   -.2521456
    InfoOnlyXRepublican |  -.2719994    .160816    -1.69   0.097    -.5950078    .0510091
 LowerPricesXRepublican |  -.2151501   .1988822    -1.08   0.285    -.6146168    .1843166
CounterChinaXRepublican |  -.1479216   .2011803    -0.74   0.466    -.5520042     .256161
                  _cons |   .6009975   .0534356    11.25   0.000      .493669     .708326
-----------------------------------------------------------------------------------------

. 
. 
. 
. *******************************************************************************
. *Table A6: Split ATT Sample Models of FavorTPP.
. 
.         **Model 1: Sample of Less Education
. reg FavorTPP InfoOnly LowerPrices CounterChina if Bachelors==0 & ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =        837
                                                F(3, 50)          =       7.87
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0286
                                                Root MSE          =     1.0356

                                 (Std. Err. adjusted for 51 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .1000809   .0992646     1.01   0.318    -.0992979    .2994596
 LowerPrices |   .4537786   .1025987     4.42   0.000      .247703    .6598541
CounterChina |   .3135281   .1028824     3.05   0.004     .1068827    .5201735
       _cons |    .441791   .0537034     8.23   0.000     .3339245    .5496576
------------------------------------------------------------------------------

. estimates store M5b

. 
.         **Model 2: Sample of More Education
. reg FavorTPP InfoOnly LowerPrices CounterChina if Bachelors==1 & ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =        724
                                                F(3, 48)          =      17.55
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0321
                                                Root MSE          =     1.1029

                                 (Std. Err. adjusted for 49 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .1910207   .1298901     1.47   0.148    -.0701408    .4521822
 LowerPrices |   .4205595   .1149278     3.66   0.001     .1894817    .6516373
CounterChina |   .4841662   .0725159     6.68   0.000     .3383633    .6299692
       _cons |   .4415094   .0647989     6.81   0.000     .3112225    .5717963
------------------------------------------------------------------------------

. estimates store M5a

. 
.         **Model 3: Sample of Republican
. reg FavorTPP InfoOnly LowerPrices CounterChina if Republican==1 & ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =        521
                                                F(3, 49)          =       2.49
                                                Prob > F          =     0.0713
                                                R-squared         =     0.0154
                                                Root MSE          =     1.1743

                                 (Std. Err. adjusted for 50 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |  -.0244492   .1505169    -0.16   0.872    -.3269243    .2780259
 LowerPrices |   .2952386   .1514303     1.95   0.057     -.009072    .5995492
CounterChina |   .2914849   .1682304     1.73   0.089    -.0465868    .6295566
       _cons |    .120603   .0913997     1.32   0.193    -.0630716    .3042776
------------------------------------------------------------------------------

. estimates store M5d

. 
.         **Model 4: Sample of Not Republican
. reg FavorTPP InfoOnly LowerPrices CounterChina if Republican==0 & ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =      1,042
                                                F(3, 49)          =      12.51
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0472
                                                Root MSE          =     .94339

                                 (Std. Err. adjusted for 50 clusters in state)
------------------------------------------------------------------------------
             |               Robust
    FavorTPP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .2475502   .0869561     2.85   0.006     .0728054     .422295
 LowerPrices |   .5103886   .1111006     4.59   0.000     .2871235    .7336537
CounterChina |   .4394065   .0785182     5.60   0.000     .2816182    .5971948
       _cons |   .6009975   .0534034    11.25   0.000     .4936794    .7083156
------------------------------------------------------------------------------

. estimates store M5c

. 
. 
. 
. *******************************************************************************
. *Figure 2: Split Sample Treatment Effects with 95% Confidence Intervals on FavorTPP from Tables A4 and A6.
. 
. coefplot (M4b, label(ITT sample) pstyle(p1) msymbol(O)) (M5b, label(ATT sample) pstyle(p1) msymbol(D)),  byl
> abel(Less Education) || (M4d, label(ITT sample) pstyle(p1) msymbol(O))  (M5d, label(ATT sample) pstyle(p1) m
> symbol(D)), bylabel(Republican) || (M4a, label(ITT sample) pstyle(p1) msymbol(O)) (M5a, label(ATT sample) ps
> tyle(p1) msymbol(D)),  bylabel(More Education) ||  (M4c, label(ITT sample) pstyle(p1) msymbol(O)) (M5c, labe
> l(ATT sample) pstyle(p1) msymbol(D)),  bylabel(Not Republican) ||, keep (InfoOnly LowerPrices CounterChina) 
>    xline(0) byopts(cols(2) graphregion(fcolor(white))) subtitle(, justification(center))  

.  
. 
. 
. *******************************************************************************
. *Table A7: Models of WroteStatement.
. 
.         **Model 1: ITT Sample with Covariates
. reg WroteStatement InfoOnly LowerPrices CounterChina FavorNAFTA Education Income Age Female White Black Asia
> n Hispanic Democrat Republican i.state, cluster (state)

Linear regression                               Number of obs     =      2,327
                                                F(13, 51)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0923
                                                Root MSE          =     .37814

                                             (Std. Err. adjusted for 52 clusters in state)
------------------------------------------------------------------------------------------
                         |               Robust
          WroteStatement |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                InfoOnly |   .0207529   .0270469     0.77   0.446    -.0335461    .0750518
             LowerPrices |   .0792501   .0235456     3.37   0.001     .0319803      .12652
            CounterChina |   .0257082   .0223176     1.15   0.255    -.0190963    .0705128
              FavorNAFTA |   .0573453   .0055515    10.33   0.000     .0462003    .0684903
               Education |   .0165938   .0098452     1.69   0.098    -.0031712    .0363588
                  Income |   .0003305   .0019432     0.17   0.866    -.0035707    .0042317
                     Age |     -.0014   .0005512    -2.54   0.014    -.0025066   -.0002934
                  Female |  -.0285728   .0151101    -1.89   0.064    -.0589076     .001762
                   White |  -.0136891   .0340398    -0.40   0.689     -.082027    .0546487
                   Black |  -.0649184   .0413009    -1.57   0.122    -.1478333    .0179966
                   Asian |  -.0210177   .0440973    -0.48   0.636    -.1095467    .0675113
                Hispanic |  -.0399292   .0262623    -1.52   0.135     -.092653    .0127947
                Democrat |   .0741003   .0205465     3.61   0.001     .0328514    .1153491
              Republican |  -.0161717   .0164728    -0.98   0.331    -.0492422    .0168988
                         |
                   state |
              Alaska AK  |   .1916088    .011443    16.74   0.000      .168636    .2145815
             Arizona AZ  |  -.1490411   .0079869   -18.66   0.000    -.1650756   -.1330067
            Arkansas AR  |    .001804   .0104367     0.17   0.863    -.0191485    .0227565
          California CA  |  -.0827927   .0108174    -7.65   0.000    -.1045095   -.0610759
            Colorado CO  |  -.0642015   .0118221    -5.43   0.000    -.0879355   -.0404676
         Connecticut CT  |  -.0970527   .0161638    -6.00   0.000    -.1295029   -.0646025
            Delaware DE  |   .2681827   .0123394    21.73   0.000     .2434104     .292955
District of Columbia DC  |    .032153    .024995     1.29   0.204    -.0180265    .0823325
             Florida FL  |  -.0805311   .0061833   -13.02   0.000    -.0929446   -.0681176
             Georgia GA  |  -.0468976   .0077916    -6.02   0.000    -.0625399   -.0312553
              Hawaii HI  |  -.3241316   .0298811   -10.85   0.000    -.3841203   -.2641429
               Idaho ID  |   .1645978   .0093395    17.62   0.000      .145848    .1833476
            Illinois IL  |  -.0917568   .0082911   -11.07   0.000     -.108402   -.0751117
             Indiana IN  |  -.2041824   .0097378   -20.97   0.000    -.2237318    -.184633
                Iowa IA  |  -.1157059   .0148175    -7.81   0.000    -.1454533   -.0859584
              Kansas KS  |  -.0948727   .0118471    -8.01   0.000    -.1186568   -.0710887
            Kentucky KY  |  -.0262847   .0074804    -3.51   0.001    -.0413021   -.0112672
           Louisiana LA  |  -.0838637   .0062594   -13.40   0.000      -.09643   -.0712974
               Maine ME  |  -.1200128   .0116272   -10.32   0.000    -.1433555   -.0966702
            Maryland MD  |  -.0197756   .0109486    -1.81   0.077    -.0417558    .0022046
       Massachusetts MA  |  -.1047756   .0108427    -9.66   0.000    -.1265432   -.0830081
            Michigan MI  |  -.1109287   .0075271   -14.74   0.000    -.1260399   -.0958175
           Minnesota MN  |  -.1727619   .0062091   -27.82   0.000    -.1852272   -.1602966
         Mississippi MS  |    .093323   .0097277     9.59   0.000     .0737938    .1128521
            Missouri MO  |  -.0954156   .0054354   -17.55   0.000    -.1063276   -.0845036
             Montana MT  |  -.2366507   .0119419   -19.82   0.000    -.2606251   -.2126763
            Nebraska NE  |  -.0863498   .0115944    -7.45   0.000    -.1096265   -.0630731
              Nevada NV  |  -.1438147   .0091265   -15.76   0.000    -.1621368   -.1254926
       New Hampshire NH  |    .047518   .0103199     4.60   0.000        .0268     .068236
          New Jersey NJ  |  -.1264396   .0109412   -11.56   0.000    -.1484051   -.1044742
          New Mexico NM  |  -.1667759   .0128024   -13.03   0.000    -.1924777   -.1410741
            New York NY  |  -.0086769   .0109682    -0.79   0.433    -.0306966    .0133427
      North Carolina NC  |  -.0995665   .0057159   -17.42   0.000    -.1110415   -.0880914
       North Dakota  ND  |  -.0376398   .0109338    -3.44   0.001    -.0595904   -.0156892
                Ohio OH  |  -.0872984   .0078063   -11.18   0.000    -.1029702   -.0716266
            Oklahoma OK  |  -.1090495   .0085519   -12.75   0.000    -.1262182   -.0918808
              Oregon OR  |  -.0977169   .0083462   -11.71   0.000    -.1144726   -.0809611
        Pennsylvania PA  |  -.1804563    .008044   -22.43   0.000    -.1966054   -.1643072
         Puerto Rico PR  |  -.4310578   .0391052   -11.02   0.000    -.5095648   -.3525509
        Rhode Island RI  |  -.2463616   .0140258   -17.56   0.000    -.2745195   -.2182037
      South Carolina SC  |   .0265178   .0097352     2.72   0.009     .0069734    .0460621
        South Dakota SD  |   .0977529   .0113298     8.63   0.000     .0750073    .1204984
           Tennessee TN  |  -.0213045   .0054625    -3.90   0.000     -.032271   -.0103379
               Texas TX  |  -.1125947   .0063358   -17.77   0.000    -.1253143    -.099875
                Utah UT  |  -.0987638   .0062394   -15.83   0.000    -.1112899   -.0862377
             Vermont VT  |  -.2578997   .0154244   -16.72   0.000    -.2888654    -.226934
            Virginia VA  |   .0933958    .007301    12.79   0.000     .0787384    .1080531
          Washington WA  |  -.1411256   .0082562   -17.09   0.000    -.1577006   -.1245506
       West Virginia WV  |   .1184902   .0087835    13.49   0.000     .1008567    .1361238
           Wisconsin WI  |    -.12026    .008468   -14.20   0.000    -.1372603   -.1032597
             Wyoming WY  |  -.3208043   .0158421   -20.25   0.000    -.3526087       -.289
                         |
                   _cons |    .226848   .0400659     5.66   0.000     .1464124    .3072835
------------------------------------------------------------------------------------------

. estimates store M11

. 
.         **Model 2: ITT Sample without Covariates
. reg WroteStatement InfoOnly LowerPrices CounterChina, cluster (state)

Linear regression                               Number of obs     =      2,404
                                                F(3, 51)          =       2.94
                                                Prob > F          =     0.0417
                                                R-squared         =     0.0052
                                                Root MSE          =     .38904

                                 (Std. Err. adjusted for 52 clusters in state)
------------------------------------------------------------------------------
             |               Robust
WroteState~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |    .029695   .0263286     1.13   0.265    -.0231619    .0825518
 LowerPrices |   .0779479   .0267124     2.92   0.005     .0243205    .1315752
CounterChina |   .0260687   .0208188     1.25   0.216    -.0157269    .0678642
       _cons |   .1533333   .0121931    12.58   0.000     .1288546    .1778121
------------------------------------------------------------------------------

. estimates store M22

. 
.         **Model 3: ATT Sample without Covariates
. reg WroteStatement InfoOnly LowerPrices CounterChina if ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =      1,563
                                                F(3, 50)          =       5.10
                                                Prob > F          =     0.0037
                                                R-squared         =     0.0128
                                                Root MSE          =     .39669

                                 (Std. Err. adjusted for 51 clusters in state)
------------------------------------------------------------------------------
             |               Robust
WroteState~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .0353459   .0271729     1.30   0.199    -.0192326    .0899244
 LowerPrices |   .1205941    .032262     3.74   0.000      .055794    .1853942
CounterChina |   .0715802   .0263754     2.71   0.009     .0186036    .1245568
       _cons |   .1533333   .0121996    12.57   0.000     .1288297    .1778369
------------------------------------------------------------------------------

. estimates store M33

. 
. 
. 
. *******************************************************************************
. *Figure 3: Treatment Effects with 95% Confidence Intervals on WroteStatement from Table A7.
. 
. coefplot (M11, label(ITT sample with covariates) pstyle(p1) msymbol(O)) (M22, label(ITT sample without covar
> iates) pstyle(p1) msymbol(S)) (M33, label(ATT sample) pstyle(p1) msymbol(D)), keep (InfoOnly LowerPrices Cou
> nterChina) xline(0) byopts(cols(1)) subtitle(, justification(left)) graphregion(fcolor(white))

. 
. 
. 
. *******************************************************************************
. *Table A8: Split ITT Sample Models of WroteStatement.
. 
.         **Model 1: Sample of Less Education
. reg WroteStatement InfoOnly LowerPrices CounterChina if Bachelors==0, cluster (state)

Linear regression                               Number of obs     =      1,318
                                                F(3, 51)          =       1.80
                                                Prob > F          =     0.1586
                                                R-squared         =     0.0052
                                                Root MSE          =     .37718

                                 (Std. Err. adjusted for 52 clusters in state)
------------------------------------------------------------------------------
             |               Robust
WroteState~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .0129264   .0278522     0.46   0.645    -.0429893     .068842
 LowerPrices |   .0671969   .0324867     2.07   0.044     .0019771    .1324167
CounterChina |   .0018397   .0242209     0.08   0.940    -.0467858    .0504653
       _cons |   .1522388   .0164479     9.26   0.000     .1192183    .1852594
------------------------------------------------------------------------------

. estimates store M7b

. 
.         **Model 2: Sample of More Education
. reg WroteStatement InfoOnly LowerPrices CounterChina if Bachelors==1, cluster (state)

Linear regression                               Number of obs     =      1,079
                                                F(3, 49)          =       2.10
                                                Prob > F          =     0.1116
                                                R-squared         =     0.0064
                                                Root MSE          =     .40303

                                 (Std. Err. adjusted for 50 clusters in state)
------------------------------------------------------------------------------
             |               Robust
WroteState~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .0528302   .0425653     1.24   0.220    -.0327079    .1383683
 LowerPrices |   .0899639   .0378451     2.38   0.021     .0139113    .1660165
CounterChina |   .0550208   .0313533     1.75   0.086    -.0079861    .1180278
       _cons |    .154717   .0228894     6.76   0.000      .108719    .2007149
------------------------------------------------------------------------------

. estimates store M7a

. 
.         **Model 3: Sample of Republican
. reg WroteStatement InfoOnly LowerPrices CounterChina if Republican==1, cluster (state)

Linear regression                               Number of obs     =        796
                                                F(3, 49)          =       3.24
                                                Prob > F          =     0.0301
                                                R-squared         =     0.0087
                                                Root MSE          =     .34047

                                 (Std. Err. adjusted for 50 clusters in state)
------------------------------------------------------------------------------
             |               Robust
WroteState~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .0265541    .038177     0.70   0.490    -.0501654    .1032736
 LowerPrices |   .0657947   .0266689     2.47   0.017     .0122015    .1193878
CounterChina |  -.0186391   .0208909    -0.89   0.377    -.0606209    .0233427
       _cons |   .1155779   .0197567     5.85   0.000     .0758754    .1552804
------------------------------------------------------------------------------

. estimates store M7d

. 
.         **Model 4: Sample of Not Republican
. reg WroteStatement InfoOnly LowerPrices CounterChina if Republican==0, cluster (state)

Linear regression                               Number of obs     =      1,608
                                                F(3, 51)          =       2.12
                                                Prob > F          =     0.1097
                                                R-squared         =     0.0055
                                                Root MSE          =     .40858

                                 (Std. Err. adjusted for 52 clusters in state)
------------------------------------------------------------------------------
             |               Robust
WroteState~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .0309005    .034714     0.89   0.378    -.0387908    .1005917
 LowerPrices |   .0848571   .0359642     2.36   0.022     .0126561    .1570582
CounterChina |    .047142   .0284323     1.66   0.103    -.0099382    .1042222
       _cons |   .1720698   .0183592     9.37   0.000     .1352122    .2089275
------------------------------------------------------------------------------

. estimates store M7c

. 
. 
. 
. *******************************************************************************
. *Table A9: Split ATT Sample Models of WroteStatement.
. 
.         **Model 1: Sample of Less Education
. reg WroteStatement InfoOnly LowerPrices CounterChina if Bachelors==0 & ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =        837
                                                F(3, 50)          =       2.28
                                                Prob > F          =     0.0910
                                                R-squared         =     0.0130
                                                Root MSE          =     .39064

                                 (Std. Err. adjusted for 51 clusters in state)
------------------------------------------------------------------------------
             |               Robust
WroteState~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |    .020175   .0311582     0.65   0.520    -.0424081    .0827581
 LowerPrices |   .1199131   .0490732     2.44   0.018     .0213466    .2184796
CounterChina |   .0605272   .0311499     1.94   0.058    -.0020393    .1230936
       _cons |   .1522388   .0164619     9.25   0.000     .1191741    .1853035
------------------------------------------------------------------------------

. estimates store M8b

. 
.         **Model 2: Sample of More Education
. reg WroteStatement InfoOnly LowerPrices CounterChina if Bachelors==1 & ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =        724
                                                F(3, 48)          =       3.50
                                                Prob > F          =     0.0225
                                                R-squared         =     0.0129
                                                Root MSE          =     .40477

                                 (Std. Err. adjusted for 49 clusters in state)
------------------------------------------------------------------------------
             |               Robust
WroteState~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .0561264   .0494679     1.13   0.262    -.0433355    .1555883
 LowerPrices |   .1211451    .038262     3.17   0.003     .0442141    .1980761
CounterChina |   .0817695   .0378994     2.16   0.036     .0055676    .1579714
       _cons |    .154717   .0229099     6.75   0.000     .1086536    .2007803
------------------------------------------------------------------------------

. estimates store M8a

. 
.         **Model 3: Sample of Republican
. reg WroteStatement InfoOnly LowerPrices CounterChina if Republican==1 & ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =        521
                                                F(3, 49)          =       2.38
                                                Prob > F          =     0.0814
                                                R-squared         =     0.0098
                                                Root MSE          =     .34872

                                 (Std. Err. adjusted for 50 clusters in state)
------------------------------------------------------------------------------
             |               Robust
WroteState~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |    .030576   .0440888     0.69   0.491    -.0580239    .1191758
 LowerPrices |   .0923429   .0346805     2.66   0.010     .0226498     .162036
CounterChina |   .0053012   .0268282     0.20   0.844     -.048612    .0592145
       _cons |   .1155779   .0197765     5.84   0.000     .0758356    .1553202
------------------------------------------------------------------------------

. estimates store M8d

. 
.         **Model 4: Sample of Not Republican
. reg WroteStatement InfoOnly LowerPrices CounterChina if Republican==0 & ManipulationCheck>0, cluster (state)

Linear regression                               Number of obs     =      1,042
                                                F(3, 49)          =       3.56
                                                Prob > F          =     0.0207
                                                R-squared         =     0.0163
                                                Root MSE          =     .41594

                                 (Std. Err. adjusted for 50 clusters in state)
------------------------------------------------------------------------------
             |               Robust
WroteState~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    InfoOnly |   .0395484   .0368386     1.07   0.288    -.0344816    .1135784
 LowerPrices |   .1348609   .0450777     2.99   0.004     .0442738    .2254479
CounterChina |   .1006574   .0377711     2.66   0.010     .0247536    .1765613
       _cons |   .1720698   .0183758     9.36   0.000     .1351424    .2089973
------------------------------------------------------------------------------

. estimates store M8c

. 
. 
. 
. *******************************************************************************
. *Figure 4: Split Sample Treatment Effects on WroteStatement with 95% Confidence Intervals from Tables A8 and
>  A9.
. 
. coefplot (M7b, label(ITT sample) pstyle(p1) msymbol(O)) (M8b, label(ATT sample) pstyle(p1) msymbol(D)),  byl
> abel(Less Education) || (M7d, label(ITT sample) pstyle(p1) msymbol(O))  (M8d, label(ATT sample) pstyle(p1) m
> symbol(D)), bylabel(Republican) || (M7a, label(ITT sample) pstyle(p1) msymbol(O)) (M8a, label(ATT sample) ps
> tyle(p1) msymbol(D)),  bylabel(More Education) ||  (M7c, label(ITT sample) pstyle(p1) msymbol(O)) (M8c, labe
> l(ATT sample) pstyle(p1) msymbol(D)),  bylabel(Not Republican) ||, keep (InfoOnly LowerPrices CounterChina) 
>    xline(0) byopts(cols(2) graphregion(fcolor(white))) subtitle(, justification(center))  

.  
. 
end of do-file

. log close
      name:  <unnamed>
       log:  /Users/seungbinpark/Dropbox/R - TPP/ISQ/ISQ -final version/replication files/Bearce & Park ISQ - 
> replication log.log
  log type:  text
 closed on:  14 Jun 2024, 13:28:58
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